Explainability Requirements for Stakeholders
5 minutes
5 Questions
In PMI-CPMAI, explainability requirements define how much insight each stakeholder group needs into how an AI system reaches its outputs. They are a core part of the Support Responsible and Trustworthy AI Efforts domain. The project manager's role is to identify these needs early, document them as formal requirements, and ensure they are met throughout the CPMAI lifecycle, not added at the end. Different stakeholders need different explanations. Executives and sponsors need high-level summaries of what the model does, its limitations, and its business risks. End users, such as loan officers or clinicians, need practical, case-level reasons that help them trust, question, or override a recommendation. Affected individuals, such as customers denied credit, may be entitled to clear, plain-language reasons and a way to contest decisions. Regulators and auditors need traceable documentation of data lineage, model logic, testing, and controls. Data scientists and engineers need technical detail for debugging, bias detection, and improvement. The project manager should capture these needs in Phase I (Business Understanding). This includes the required explanation type, either global (overall model behavior) or local (individual predictions), the format, the level of detail, and any legal drivers such as GDPR, the EU AI Act, or industry rules. These requirements shape later choices. In high-stakes contexts, a team may choose an inherently interpretable model over a slightly more accurate black-box model. Otherwise, it may plan post-hoc techniques such as SHAP, LIME, feature importance, or counterfactual explanations. During Model Evaluation, teams confirm that the explanations are accurate, consistent, and understandable to their intended audiences. During Operationalization, explanations are monitored as models drift, and artifacts such as model cards, decision logs, and user guidance are kept current. Clear explainability requirements build trust, support accountability and human oversight, enable bias detection, reduce regulatory risk, and increase adoption. Together, they make AI solutions both effective and responsible.
Explainability Requirements for Stakeholders (PMI-CPMAI: Support Responsible and Trustworthy AI Efforts)
Overview
Explainability requirements for stakeholders sit in the Support Responsible and Trustworthy AI Efforts domain of the PMI Certified Professional in Managing AI (PMI-CPMAI) framework.
The idea is simple. Different people need different kinds of explanations about how an AI system reaches its outputs. The project team must find, document and deliver those explanations deliberately. Explainability is not an afterthought or a purely technical feature. It is a requirement that is defined early, managed through the life cycle and validated before and after deployment.
Why It Is Important
1. Trust and adoption: Users who cannot understand or question AI outputs tend to reject the system or over-rely on it blindly. Fitting explanations build calibrated trust.
2. Regulatory and legal compliance: Many regulations expect meaningful information about automated decisions. Examples include GDPR provisions on automated decision-making, the EU AI Act transparency duties for high-risk systems, fair lending rules requiring adverse action reasons, and healthcare and insurance rules. Missing this exposes the organization to fines and litigation.
3. Accountability and auditability: Auditors, risk officers and regulators need traceable reasoning to check that decisions are fair, lawful and consistent with policy.
4. Bias detection and fairness: Explanations reveal whether a model relies on proxies for protected attributes or on spurious correlations.
5. Debugging and model improvement: Data scientists and engineers use explanations to diagnose errors, data drift and weak features.
6. Ethical obligation and the right to recourse: People affected by decisions (loan applicants, patients, job candidates) deserve to know why a decision was made and what they can do about it.
7. Project success: In CPMAI thinking, AI projects often fail because business needs, including transparency needs, were not captured early. Unmet explainability needs can block go-live approval.
What It Is
Explainability is the ability to describe, in terms a given audience can understand, why an AI system produced a particular output or how it behaves overall. Several related terms are often confused:
- Transparency: Openness about the system's existence, purpose, data, design, limitations and governance. It answers what is this system and how was it built?
- Interpretability: How far a human can directly understand the model's internal mechanics. Linear models and shallow decision trees are inherently interpretable.
- Explainability: Producing understandable reasons for outputs, often through post-hoc techniques for complex models. It answers why did it decide this?
An explainability requirement is a documented, testable statement. It specifies:
- who needs an explanation
- what they need explained (global behavior or an individual decision)
- why they need it (compliance, trust, recourse, debugging)
- in what form and level of detail
- when and how it is delivered
- how its adequacy will be measured
Key Stakeholder Groups and Their Typical Needs
- Executive sponsors and business owners: High-level understanding of what drives outcomes, business risk, limitations and alignment with strategy. Dashboards and summaries.
- End users and operators (loan officers, clinicians, call center staff): Actionable, in-context reasons for each recommendation, confidence levels, and guidance on when to override.
- Affected individuals (customers, patients, applicants): Plain-language reasons for decisions affecting them, the main factors involved, and how to contest or improve outcomes (counterfactuals such as if your income were X, you would be approved).
- Regulators, auditors and compliance or legal teams: Documentation of data lineage, model logic, validation results, fairness testing and decision logs. Model cards, datasheets and audit trails.
- Data scientists and engineers: Detailed technical explanations, such as feature importance, SHAP values, partial dependence plots and error analysis, for debugging and monitoring.
- Risk, ethics and governance boards: Evidence that explanations meet policy thresholds and that risks of opacity have been mitigated.
- Support and customer service teams: Scripts and tools for explaining outcomes to customers consistently.
How It Works: The Process
Step 1: Identify stakeholders and assess impact. During business understanding (CPMAI Phase I), map everyone who builds, uses, oversees or is affected by the AI. Classify the use case by risk level. High-stakes decisions in credit, hiring, health or justice demand stronger explainability.
Step 2: Elicit explainability needs. Use interviews, workshops, personas and regulatory analysis. For each stakeholder, ask:
- What decisions will you make with this output?
- What must you justify, and to whom?
- What laws or policies apply?
- What is your technical literacy?
Step 3: Document requirements. Record them in the requirements backlog or traceability matrix with acceptance criteria. For example: For every declined application, the system shall provide the top four contributing factors in plain language within the adverse action notice; comprehension validated with at least 80 percent of test users.
Step 4: Make the accuracy versus explainability trade-off. Requirements shape model selection in data understanding, preparation and modeling (CPMAI Phases II to IV). If strong explainability is mandated, a simpler interpretable model may be preferred over a slightly more accurate black box. Otherwise, post-hoc methods must be planned:
- LIME and SHAP
- counterfactual explanations
- attention visualization
- surrogate models
Document trade-off decisions and get stakeholder sign-off.
Step 5: Design explanation delivery. Tailor the format, such as UI tooltips, reason codes, reports, model cards or API fields. Explanations should be accurate (faithful to the model), understandable, actionable, timely and consistent.
Step 6: Validate and test. In model evaluation (CPMAI Phase V), test both technical faithfulness and human comprehension. Run user testing, compliance review and an ethics board check. Explainability acceptance criteria are part of go or no-go decisions.
Step 7: Operationalize and monitor. In operationalization (CPMAI Phase VI), log explanations for audit. Monitor drift that may change explanations, collect stakeholder feedback, and update documentation when models are retrained.
Step 8: Govern continuously. Assign ownership (for example, a model owner or responsible AI lead). Keep documentation current and revisit requirements when regulations, users or use cases change. CPMAI is iterative, so explainability requirements are revisited each iteration.
Global vs. Local Explanations
- Global: How the model behaves overall, such as which features matter most across all predictions. Useful for executives, regulators and governance.
- Local: Why a specific prediction was made for a specific case. Essential for end users and affected individuals.
Many exam scenarios hinge on choosing the right one for the right audience.
Common Artifacts
- Model cards
- Datasheets for datasets
- Decision logs and audit trails
- Reason codes
- Explainability section of the AI risk assessment
- Stakeholder communication plan
- Requirements traceability matrix
- Transparency notices for the public
Exam Tips: Answering Questions on Explainability Requirements for Stakeholders
1. Tailor to the audience. The best answer almost always matches the explanation type and depth to the specific stakeholder. Be wary of options that give everyone the same technical output, such as raw SHAP plots for customers, or that give regulators only marketing summaries.
2. Think early and proactive. PMI favors defining explainability requirements during business understanding and requirements gathering, not after deployment or after a complaint. If an option says identify and document explainability needs with stakeholders at project start, it is usually strong.
3. Consult before you decide. In scenario questions, the right first step is often to engage the stakeholders, compliance or legal, or the governance board to clarify needs. Unilaterally swapping models or shipping is usually wrong.
4. Recognize the trade-off. When high accuracy conflicts with mandated explainability in a high-risk, regulated context, the answer typically favors meeting explainability and compliance. Options include an interpretable model or adding validated explanation methods, with a documented, stakeholder-approved decision. Never ignore regulatory explainability for a small accuracy gain.
5. Match risk to rigor. Low-risk uses (movie recommendations) need lighter explainability. High-impact uses (credit, medical, hiring) need robust, individual-level, contestable explanations. Choose proportional answers.
6. Distinguish the terms. Know transparency (openness about the system), interpretability (inherently understandable mechanics) and explainability (reasons for outputs). Know global versus local, and post-hoc versus intrinsic.
7. Requirements must be testable. Prefer answers that set measurable acceptance criteria and validate explanations with real users. Vague goals like make the model transparent are weaker.
8. Look for recourse and contestability. For affected individuals, the best answers include actionable reasons and a path to appeal or human review.
9. Remember the full life cycle. Explainability continues after deployment: logging, monitoring, updating explanations after retraining and handling stakeholder feedback. Avoid options implying it is a one-time deliverable.
10. Watch for distractors.
- Revealing proprietary source code to customers is rarely required or appropriate.
- Assuming that high accuracy removes the need for explanation.
- Relying solely on data scientists to decide what stakeholders need.
- Treating disclaimers as a substitute for explanations.
11. Link to the broader trustworthy AI themes. Explainability supports fairness, accountability, privacy-aware transparency and human oversight. Answers that integrate explainability with governance, risk management and communication plans are usually best.
12. Use a decision heuristic. Ask yourself:
- Who is the stakeholder?
- What decision or obligation drives their need?
- What risk level applies?
- Which option delivers an appropriate, faithful, understandable and documented explanation through a collaborative, life-cycle-aware process?
Pick that option.
Sample Scenario
A bank's AI credit model outperforms the legacy scorecard, but compliance says applicants must receive specific reasons for denials. What should the project manager do first?
Best answer: work with compliance and business stakeholders to define explainability requirements for adverse action reasons. Then evaluate whether the model, with validated local explanation methods or an interpretable alternative, can meet them before deployment.
Wrong answers include:
- deploying because accuracy is higher
- providing generic denial letters
- sending applicants technical feature importance charts
Summary
Explainability requirements for stakeholders mean systematically identifying who needs to understand an AI system, what they need to understand and why. Those needs are then turned into documented, testable requirements that shape model choice, design, validation and operations. On the exam, favor answers that are stakeholder-specific, proactive, risk-proportionate, compliant, collaborative, measurable and sustained across the AI life cycle.
Explainability requirements for stakeholders sit in the Support Responsible and Trustworthy AI Efforts domain of the PMI Certified Professional in Managing AI (PMI-CPMAI) framework.
The idea is simple. Different people need different kinds of explanations about how an AI system reaches its outputs. The project team must find, document and deliver those explanations deliberately. Explainability is not an afterthought or a purely technical feature. It is a requirement that is defined early, managed through the life cycle and validated before and after deployment.
Why It Is Important
1. Trust and adoption: Users who cannot understand or question AI outputs tend to reject the system or over-rely on it blindly. Fitting explanations build calibrated trust.
2. Regulatory and legal compliance: Many regulations expect meaningful information about automated decisions. Examples include GDPR provisions on automated decision-making, the EU AI Act transparency duties for high-risk systems, fair lending rules requiring adverse action reasons, and healthcare and insurance rules. Missing this exposes the organization to fines and litigation.
3. Accountability and auditability: Auditors, risk officers and regulators need traceable reasoning to check that decisions are fair, lawful and consistent with policy.
4. Bias detection and fairness: Explanations reveal whether a model relies on proxies for protected attributes or on spurious correlations.
5. Debugging and model improvement: Data scientists and engineers use explanations to diagnose errors, data drift and weak features.
6. Ethical obligation and the right to recourse: People affected by decisions (loan applicants, patients, job candidates) deserve to know why a decision was made and what they can do about it.
7. Project success: In CPMAI thinking, AI projects often fail because business needs, including transparency needs, were not captured early. Unmet explainability needs can block go-live approval.
What It Is
Explainability is the ability to describe, in terms a given audience can understand, why an AI system produced a particular output or how it behaves overall. Several related terms are often confused:
- Transparency: Openness about the system's existence, purpose, data, design, limitations and governance. It answers what is this system and how was it built?
- Interpretability: How far a human can directly understand the model's internal mechanics. Linear models and shallow decision trees are inherently interpretable.
- Explainability: Producing understandable reasons for outputs, often through post-hoc techniques for complex models. It answers why did it decide this?
An explainability requirement is a documented, testable statement. It specifies:
- who needs an explanation
- what they need explained (global behavior or an individual decision)
- why they need it (compliance, trust, recourse, debugging)
- in what form and level of detail
- when and how it is delivered
- how its adequacy will be measured
Key Stakeholder Groups and Their Typical Needs
- Executive sponsors and business owners: High-level understanding of what drives outcomes, business risk, limitations and alignment with strategy. Dashboards and summaries.
- End users and operators (loan officers, clinicians, call center staff): Actionable, in-context reasons for each recommendation, confidence levels, and guidance on when to override.
- Affected individuals (customers, patients, applicants): Plain-language reasons for decisions affecting them, the main factors involved, and how to contest or improve outcomes (counterfactuals such as if your income were X, you would be approved).
- Regulators, auditors and compliance or legal teams: Documentation of data lineage, model logic, validation results, fairness testing and decision logs. Model cards, datasheets and audit trails.
- Data scientists and engineers: Detailed technical explanations, such as feature importance, SHAP values, partial dependence plots and error analysis, for debugging and monitoring.
- Risk, ethics and governance boards: Evidence that explanations meet policy thresholds and that risks of opacity have been mitigated.
- Support and customer service teams: Scripts and tools for explaining outcomes to customers consistently.
How It Works: The Process
Step 1: Identify stakeholders and assess impact. During business understanding (CPMAI Phase I), map everyone who builds, uses, oversees or is affected by the AI. Classify the use case by risk level. High-stakes decisions in credit, hiring, health or justice demand stronger explainability.
Step 2: Elicit explainability needs. Use interviews, workshops, personas and regulatory analysis. For each stakeholder, ask:
- What decisions will you make with this output?
- What must you justify, and to whom?
- What laws or policies apply?
- What is your technical literacy?
Step 3: Document requirements. Record them in the requirements backlog or traceability matrix with acceptance criteria. For example: For every declined application, the system shall provide the top four contributing factors in plain language within the adverse action notice; comprehension validated with at least 80 percent of test users.
Step 4: Make the accuracy versus explainability trade-off. Requirements shape model selection in data understanding, preparation and modeling (CPMAI Phases II to IV). If strong explainability is mandated, a simpler interpretable model may be preferred over a slightly more accurate black box. Otherwise, post-hoc methods must be planned:
- LIME and SHAP
- counterfactual explanations
- attention visualization
- surrogate models
Document trade-off decisions and get stakeholder sign-off.
Step 5: Design explanation delivery. Tailor the format, such as UI tooltips, reason codes, reports, model cards or API fields. Explanations should be accurate (faithful to the model), understandable, actionable, timely and consistent.
Step 6: Validate and test. In model evaluation (CPMAI Phase V), test both technical faithfulness and human comprehension. Run user testing, compliance review and an ethics board check. Explainability acceptance criteria are part of go or no-go decisions.
Step 7: Operationalize and monitor. In operationalization (CPMAI Phase VI), log explanations for audit. Monitor drift that may change explanations, collect stakeholder feedback, and update documentation when models are retrained.
Step 8: Govern continuously. Assign ownership (for example, a model owner or responsible AI lead). Keep documentation current and revisit requirements when regulations, users or use cases change. CPMAI is iterative, so explainability requirements are revisited each iteration.
Global vs. Local Explanations
- Global: How the model behaves overall, such as which features matter most across all predictions. Useful for executives, regulators and governance.
- Local: Why a specific prediction was made for a specific case. Essential for end users and affected individuals.
Many exam scenarios hinge on choosing the right one for the right audience.
Common Artifacts
- Model cards
- Datasheets for datasets
- Decision logs and audit trails
- Reason codes
- Explainability section of the AI risk assessment
- Stakeholder communication plan
- Requirements traceability matrix
- Transparency notices for the public
Exam Tips: Answering Questions on Explainability Requirements for Stakeholders
1. Tailor to the audience. The best answer almost always matches the explanation type and depth to the specific stakeholder. Be wary of options that give everyone the same technical output, such as raw SHAP plots for customers, or that give regulators only marketing summaries.
2. Think early and proactive. PMI favors defining explainability requirements during business understanding and requirements gathering, not after deployment or after a complaint. If an option says identify and document explainability needs with stakeholders at project start, it is usually strong.
3. Consult before you decide. In scenario questions, the right first step is often to engage the stakeholders, compliance or legal, or the governance board to clarify needs. Unilaterally swapping models or shipping is usually wrong.
4. Recognize the trade-off. When high accuracy conflicts with mandated explainability in a high-risk, regulated context, the answer typically favors meeting explainability and compliance. Options include an interpretable model or adding validated explanation methods, with a documented, stakeholder-approved decision. Never ignore regulatory explainability for a small accuracy gain.
5. Match risk to rigor. Low-risk uses (movie recommendations) need lighter explainability. High-impact uses (credit, medical, hiring) need robust, individual-level, contestable explanations. Choose proportional answers.
6. Distinguish the terms. Know transparency (openness about the system), interpretability (inherently understandable mechanics) and explainability (reasons for outputs). Know global versus local, and post-hoc versus intrinsic.
7. Requirements must be testable. Prefer answers that set measurable acceptance criteria and validate explanations with real users. Vague goals like make the model transparent are weaker.
8. Look for recourse and contestability. For affected individuals, the best answers include actionable reasons and a path to appeal or human review.
9. Remember the full life cycle. Explainability continues after deployment: logging, monitoring, updating explanations after retraining and handling stakeholder feedback. Avoid options implying it is a one-time deliverable.
10. Watch for distractors.
- Revealing proprietary source code to customers is rarely required or appropriate.
- Assuming that high accuracy removes the need for explanation.
- Relying solely on data scientists to decide what stakeholders need.
- Treating disclaimers as a substitute for explanations.
11. Link to the broader trustworthy AI themes. Explainability supports fairness, accountability, privacy-aware transparency and human oversight. Answers that integrate explainability with governance, risk management and communication plans are usually best.
12. Use a decision heuristic. Ask yourself:
- Who is the stakeholder?
- What decision or obligation drives their need?
- What risk level applies?
- Which option delivers an appropriate, faithful, understandable and documented explanation through a collaborative, life-cycle-aware process?
Pick that option.
Sample Scenario
A bank's AI credit model outperforms the legacy scorecard, but compliance says applicants must receive specific reasons for denials. What should the project manager do first?
Best answer: work with compliance and business stakeholders to define explainability requirements for adverse action reasons. Then evaluate whether the model, with validated local explanation methods or an interpretable alternative, can meet them before deployment.
Wrong answers include:
- deploying because accuracy is higher
- providing generic denial letters
- sending applicants technical feature importance charts
Summary
Explainability requirements for stakeholders mean systematically identifying who needs to understand an AI system, what they need to understand and why. Those needs are then turned into documented, testable requirements that shape model choice, design, validation and operations. On the exam, favor answers that are stakeholder-specific, proactive, risk-proportionate, compliant, collaborative, measurable and sustained across the AI life cycle.
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18 questions (total)
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